Echo VS prometheus

Echo - web framework for Go

The three behaviors I've described that we want all depend on two things, the first of which is "idiomatic error handling". We need to be able to simply return err in our handlers. Unfortunately, the standard libray doesn't give us this. But some third-party frameworks do. The most popular one I'm familiar with is labstack echo, whose HandlerFunc looks like this:

In this tutorial, I will be using the Echo framework to build the backend. You can learn more about Echo here.

____ __ / __/___/ / ___ / _// __/ _ \/ _ \ /___/\__/_//_/\___/ v4.11.1 High performance, minimalist Go web framework https://echo.labstack.com ____________________________________O/_______ O\ ⇨ http server started on [::]:8080

Some of the features: - ✅ Using Vertical Slice Architecture as a high level architecture - ✅ Using Event Driven Architecture on top of RabbitMQ Message Broker with a custom [Event Bus](pkg/messaging/bus/) - ✅ Using Event Sourcing in Audit Based services like [Orders Service](services/orders/) - ✅ Using CQRS Pattern and Mediator Patternon top of Go-MediatR library - ✅ Using Dependency Injection and Inversion of Controlon top of uber-go/fx library - ✅ Using RESTFul api with Echo framework and using swagger with swaggo/swag library - ✅ Using Postgres and EventStoreDB to write databases with fully supports transactions(ACID) - ✅ Using MongoDB and Elastic Search for read databases (NOSQL) - ✅ Using OpenTelemetry for collection Distributed Tracing with using Jaeger and Zipkin - ✅ Using OpenTelemetry for collection Metrics with using Prometheus and Grafana - ✅ Using Unit Test for testing small units with mocking dependent classes and using Mockery for mocking dependencies - ✅ Using End2End Test and Integration Test for testing features with all of their real dependeinces using docker containers (cleanup tests) and testcontainers-go library

If you come from NodeJS background, you may find Echo (https://echo.labstack.com) most similar to express.

Can I try to rewrite it using the following? I'll just hand you the code I don't care about credit, I just enjoy cleaning things up. - https://github.com/spf13/cobra - https://echo.labstack.com/ - SQLite - and not a bunch of if statements

Use a library for HTTP serving, such as Gin, Chi, or Echo. I personally use Chi, as it's just the right level of abstraction for how I like to work. Despite what others say here, don't try to re-implement everything in a modern serving library using the standard library.

Here I'm not really sure what you're referring to: * You can set request timeout and it has nothing to do with whether you handled your error or not. * In most cases you either bubble it up the callstack or do something with error in place you o received it i.e. you switch to default value, retry or sth along those lines. In some cases frameworks like echo will translate error into 5XX response for you if you don't do anything with it in top level handler. * Panics are recoverable. Also in case your handler panics it won't crash entire server -> stdlib HTTP server just closes connection, frameworks might even provide panic handler which will return 5XX instead of nothing. * try/catch doesn't really solve anything I mentioned here ¯_(ツ)_/¯. You just hope somebody caught your exception somewhere else.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.